Industrial Engineering Commons^™

Modeling Individual Self-Protective Behavior During Epidemics, Geonsik Yu, Michael J. Garee, Mario Ventresca, Yuehwern Yih

Faculty Publications

Protecting public health from infectious diseases requires collective action, as individual behaviors—such as vaccination and mask-wearing—directly influence disease dynamics. During the COVID-19 pandemic, unexpected public responses often undermined the effectiveness of interventions, highlighting the need to understand collective behavioral patterns and motivations to design more effective mitigation strategies. This study presents an agent-based simulation model that captures how individuals adjust self-protective behaviors based on evolving opinions about disease risk and examines how these decisions interact with external factors, such as public health interventions, to shape collective outcomes. To improve the representativeness of the simulated population, multiple datasets were integrated to …

Proactive Safety Reasoning In Human-Robot Collaboration In Disassembly Through Llm-Augmented Stpa And Fmea, Morteza Jalali Alenjareghi, Fardin Ghorbani, Samira Keivanpour, Yuvin Adnarain Chinniah, Sabrina Jocelyn

Études primaires

Disassembly tasks in human–robot collaboration (HRC) environments present safety challenges due to hazardous materials, control system variability, and physically demanding operator tasks. To address these challenges, we propose an AI-augmented risk assessment framework integrating System-Theoretic Process Analysis (STPA) and Failure Mode and Effects Analysis (FMEA). This framework is implemented in four configurations: Term Frequency– Inverse Document Frequency (TF-IDF), Fine-tuned Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and RAG with a structured Knowledge Graph (KG) built from safety standards. The system supports real-time, standards-compliant safety reasoning by generating interpretable, context-specific recommendations. We evaluate these configurations across GPT-3.5 TURBO, GPT-4o, GPT-4.1, and …

A Study Of Perceptions, Readiness, Benefits, And Barriers Related To Exoskeleton Adoption In New Jersey’S Warehousing Sector, Terry Asante

Theses

The warehousing industry in New Jersey remains a vital component of the region's logistics network, employing more than 200,000 workers who routinely engage in lifting, bending, overhead reaching, and other physically demanding activities. These exposures contribute to musculoskeletal disorder (MSD) rates that exceed national averages, particularly affecting the low back and shoulders. Nationally, MSDs account for an estimated $420 billion in combined direct and indirect costs each year, underscoring the need for interventions that can effectively reduce biomechanical strain. Industrial exoskeletons have emerged as a potential solution, with prior research demonstrating reductions in muscle activation, perceived exertion, and fatigue during …

Transparent Eeg Analysis: Leveraging Autoencoders, Bi-Lstms, And Shap For Improved Neurodegenerative Diseases Detection, Badr Mouazen, Ahmed Bendaouia, Omaima Bellakhdar, Khaoula Laghdaf, Aya Ennair, El Hassan Abdelwahed, Giovanni De Marco

Manufacturing & Industrial Engineering Faculty Publications

Highlights

• Novel hybrid architecture: Combined autoencoders with bidirectional LSTM networks for enhanced EEG signal classification, achieving 98% accuracy in distinguishing AD, FTD, and healthy controls.

• Explainable AI integration: Implemented SHAP (SHapley Additive exPlanations) framework to enhance model transparency and identify entropy as the most influential feature for neurodegenerative disease detection.

• Optimal temporal segmentation: Demonstrated that 5-s EEG windows with 50% overlap provide the best balance between classification accuracy and computational efficiency.

• Comprehensive feature extraction: Utilized Power Spectral Density (PSD) analysis across standard frequency bands (Delta, Theta, Alpha, Beta, Gamma) following autoencoder-based dimensionality reduction.

• Superior performance validation: Outperformed traditional machine learning …

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Leveraging Physiological Signal Activity And Self-Report Data To Assess Students’ Trust In “My Friendly Mind” App And Its Impact On Their Mental Health Knowledge: A Mixed-Method Phase 1 Clinical Trial Focusing On Depression And Attention Deficit Hyperactivity Disorder From Human Factors Standpoint., Yeganeh Shahsavar

Graduate Theses, Dissertations, and Problem Reports (ETD)

Mental health issues have become a significant global public health concern, especially among younger generations. The growing number of mental health challenges, combined with limited access to quality care, makes the problem even worse. Studies reveal that over 70% of individuals worldwide in need of mental health services do not receive appropriate care. Digital health technologies have the potential to enhance mental health services by making them more accessible and affordable. Despite the increasing popularity of mental health mobile applications (mHealth), there remains a lack of robust evidence of their effectiveness and the level of user trust, particularly in areas …

Exploring Healthcare Chatbot Information Presentation: Applying Hierarchical Bayesian Regression And Inductive Thematic Analysis In A Mixed Methods Study, Samuel Nelson Koscelny

All Theses

High blood pressure, also known as hypertension, significantly increases the risk of heart disease and stroke, which are leading causes of death in the United States. While contributing to over 691,000 deaths in 2021 alone in the United States (U.S.), it also imposes immense economic burden on the healthcare system, costing approximately $131 billion annually. One way to address this issue is for increased self-care behaviors and medication adherence, both of which require sufficient health literacy. Despite the importance of health literacy, 90% of U.S. adults struggle with health-related subjects. Overcoming the issues associated with health literacy requires addressing the …

Integrating Machine Learning And Simulation For Resource Planning Of Hospital Systems Based On Predicted Length Of Stay, S M Atikur Rahman

Open Access Theses & Dissertations

Recently Hospital Systems faced a high invasion of patients generated by several events such as health crisis related epidemic (COVID, FLU) or seasonal flows. Hence, managing hospital bed availability and efficiency with proper care is obligatory for addressing the challenges associated with the overburden of patients. However, the Length of stay (LOS) is often increased due to the high patient influx and overcrowding problem occurs within the Hospital. It resolves these issues, it is essential for hospital authority to predict the Patients LOS which is the crucial indicator for the use of medical resources (allocation, utilization of providers and resource) …

Reduction Of The Length Of Stay At The Emergency Department Of The Audie L. Murphy Hospital, Arwa Al Shikrian

Theses

We implemented the DMAIC framework (Define, Measure, Analyze, Improve, and Control) and Lean Six Sigma methods to reduce the length of stay (LOS) in the Emergency Department (ED) at Audie L. Murphy VA Hospital, a crucial issue affecting operational efficiency and patient care quality. This project was conducted in conjunction with an internship at the Hospital; our efforts were an integral part of a Green Belt project being conducted by Mr. Roarke Verkaik-Bushby, Hospital Administration Service, to

whom this author reported.

We employed quantitative research design, meticulously observing, measuring, and analyzing ED processes. We tracked patient flow, identified bottlenecks, and …

Mathematical Modeling For Dental Decay Prevention In Children And Adolescents, Mahdiyeh Soltaninejad

Dissertations

The high prevalence of dental caries among children and adolescents, especially those from lower socio-economic backgrounds, is a significant nationwide health concern. Early prevention, such as dental sealants and fluoride varnish (FV), is essential, but access to this care remains limited and disparate. In this research, a national dataset is utilized to assess sealants' reach and effectiveness in preventing tooth decay, particularly focusing on 2nd molars that emerge during early adolescence, a current gap in the knowledge base. FV is recommended to be delivered during medical well-child visits to children who are not seeing a dentist. Challenges and facilitators in …

Changes In Psychiatric Diagnosis Associated With Sars-Cov-2 Infection And Predicting The Development Of New Psychiatric Illness In Covid Patients By Using Machine Learning Approach: A Study Using The Us National Covid Cohort Collaborative (N3c), Asif Rahman

Graduate Theses, Dissertations, and Problem Reports (ETD)

The enduring impact of COVID-19 extends beyond acute illness, with potential long-term psychiatric consequences raising significant concern among healthcare professionals and researchers alike. Emerging evidence suggests a multifaceted relationship between COVID-19 and the development of different psychiatric illnesses like Schizophrenia Spectrum and Psychotic Disorders (SSPD), Depression, Bipolar disorder, Personality disorder, Trauma, and a range of other mental health conditions. Considering these emerging connections, our study endeavors to rigorously assess the associations between COVID-19 and various psychiatric illnesses while simultaneously employing machine learning techniques to predict the development of new psychiatric disorders in individuals affected by the virus. Leveraging the extensive …

Laser-Induced Forward Transfer (Lift) Based Bioprinting Of The Collagen I With Retina Photoreceptor Cells, Md Shakil Arman, Ben Xu, Andrew Tsin, Jianzhi Li

Manufacturing & Industrial Engineering Faculty Publications

This study focuses on the 3D bioprinting of retina photoreceptor cells using a laser-induced forward transfer (LIFT) based bioprinting system. Bioprinting has a great potential to mimic and regenerate the human organoid system, and the LIFT technique has emerged as an efficient method for high-resolution micropatterning and microfabrication of biomaterials and cells due to its capability of creating precise, controlled microdroplets. In this study, the parameters for an effective femtosecond laser-based LIFT process for 3D bioprinting of collagen biomaterial were studied. Different concentrations of collagen I solutions were tested and 0.75 mg/ml to 1 mg/ml collagen Ⅰ was identified as …

Variability In Anesthesia And Its Implications For Improving Patient Safety, Joshua Michael Biro

All Dissertations

Improving patient safety in anesthesia has proven to be an arduous and challenging task. Despite the many strategies and interventions to improve patient safety that have been employed, patient harm in anesthesia remains a problem. The struggle to reduce patient harm in anesthesia is both attributable to and representative of the complexity of the anesthesia system. In navigating this complex system, anesthesia providers have different approaches to how they accomplish their work, which results in variability in anesthesia practice. This variability provides an immense challenge to designing and implementing efforts to improve patient safety, as rigid interventions are often met …

Detecting Pathobiomes Using Machine Learning, Valerie Jackson, Valerie Jackson

Industrial Engineering Undergraduate Honors Theses

Machine learning is a field with high growth potential due to the overall continuous progressions, developments, advancements, and improvements caused by the way it is used to help interpret and use large amounts of data [1]. One type of data that can be collected and analyzed by these machine learning models is data that is associated with DNA and information that the DNA gives. The research will be focusing specifically on using machine learning technology to detect pathobiomes indicative of salmonella pork. The pathobiome associated with salmonella is very similar to others, and this causes a problem for classification/detection with …

Digital Patient Engagement At A Perioperative Surgical Home Implemented Community Hospital, Srinivasan Sridhar, Amy Mount Hunter, Bernadette Mccrory

Patient Experience Journal

Patients in rural areas typically require more perioperative ‘optimization’ for surgery. The rural healthcare systems often overwhelmed with coordinating perioperative services and deliver less than optimal surgical outcomes. This is due to limited supporting microsystems and ability to effectively engage and track patients over the 120-day perioperative period to limit post-surgical complications. The study assessed longitudinal patient engagement within a newly established Perioperative Surgical Home (PSH) at a rural community hospital serving 10+ surrounding counties to identify barriers and best practices for engagement. A digital patient engagement platform was implemented and used to assess longitudinal patient outcomes and engagement from …

Introduction To Bioaerosols Assessment And Control, 2nd Edition, Cheri Marcham, John (Jack) Springston

Publications

• Risk Assessment
• Assessment for the Presence of Bioaerosols
• Sampling
• Purpose/ Necessity
• Interpretation Controls
  • Ventilation
  • Other Controls

Lignin Derived Hydrophobic Deep Eutectic Solvents As Sustainable Extractants, Yuxuan Zhang, Qi Qiao, Usman Lame Abbas, Jun Liu, Yi Zheng, Christopher Jones, Qing Shao, Jian Shi

Markey Cancer Center Faculty Publications

Solvent innovation has become a central task for improving the sustainability of chemical processes1. Deep eutectic solvents (DESs) emerge as environmentally friendly alternatives to toxic and volatile organic solvents. One appealing aspect for DESs is that they can be synthesized using naturally occurring compounds from biomass. Herein, we prepared novel hydrophobic DESs based on lignin derivatives and characterized their physicochemical properties including density, viscosity, and thermal behavior. The results showed that five lignin-derived hydrophobic DESs made from menthol, thymol, and 2,6-dimethoxyphenol were promising as green solvents due to their low viscosities and environmentally friendly constituents. To evaluate the potential application …

Modeling Behavior And Vaccine Hesitancy For Predicting Daily Vaccination Inoculations Using Trends, Case, Death, And Twitter Sentiment Data, Talal Daghriri

Electronic Theses and Dissertations, 2020-2023

Over the past 100 years, epidemiological models have evolved to incorporate greater facets of the process. With the advent of social networking, massive computational power, population sentiment analysis can now be added to the epidemiological modeling process. Sentiment analysis is greater understanding of the fears, uncertainties, motivation, and trends of the public with respect to vaccination and associated events. Lack of public confidence in the efficacy of models, safety of vaccines, and appropriateness of policies confounds vaccine inoculation prediction. Sentiment analysis of social media is a seminal technique that accesses shared users' contents and tweets on the Twitter platform for …

A Machine Learning Approach For Early Diagnosis Of Transthyretin Amyloid Cardiomyopathy Among Heart Failure Patients, Tanjim Ahmed

Graduate Theses, Dissertations, and Problem Reports (ETD)

Transthyretin Amyloid Cardiomyopathy (ATTR-CM) is a rare, progressive, and fatal disease. Prevalence of ATTR-CM ranges from 4 to 17 per 100000 cases where the mean survival time is less than 4 years. It has a history of being underdiagnosed and misdiagnosed. The diagnosis delay has a weighted mean of 6.1 years for wild-type ATTR-CM. Low awareness, the necessity of invasive procedures, and lack of treatment are the key reasons for delayed diagnosis. But, with the introduction of non-invasive tests like nuclear scintigraphy with ^99mTC-PYP and the disease modifying drug Tafamidis, the diagnosis delay signifies a missed opportunity to increase …

Models And Algorithms For Trauma Network Design., Sagarkumar Dhirubhai Hirpara

Electronic Theses and Dissertations

Trauma continues to be the leading cause of death and disability in the US for people aged 44 and under, making it a major public health problem. The geographical maldistribution of Trauma Centers (TCs), and the resulting higher access time to the nearest TC, has been shown to impact trauma patient safety and increase disability or mortality. State governments often design a trauma network to provide prompt and definitive care to their citizens. However, this process is mainly manual and experience-based and often leads to a suboptimal network in terms of patient safety and resource utilization. This dissertation fills important …

Eagle Medical Tray Denesting & Debris Removal Process, Nicholas Allen Ungefug, Noah Chavez, Susana Shu-Lin Okhuysen, Michael Augustine Pennington

Industrial and Manufacturing Engineering

Eagle Medical Incorporated is a contract medical device packaging and sterilization company. The company purchases thermoformed medical packaging trays, which maintain the sterility of medical devices, from various manufacturers. To ensure packaging quality and to prevent cleanroom contamination, Eagle Medical inspects and sterilizes each blister tray that they order. This process is an essential non-value-added activity that creates a bottleneck. Cleanroom employees must stop packaging medical devices and attend to the processing of blister trays and packaging solutions. The blister trays arrive at Eagle’s facility in nested stacks. Vibration and movement during shipping further compresses the stacks, which makes separation …

Supplier Performance Scorecard Utilization In The Medical Device Manufacturing Healthcare Supply Chain, Justin Cardisco

Theses and Dissertations

The medical device manufacturing industry has a deficiency in determining how to improve supplier performance for the components and systems they purchase. Many complex medical devices require components from superb suppliers. But how does a medical device manufacturer (MDM) impartially assess supplier performance to know which suppliers to continuing with (or even boost purchase volumes) and which suppliers they should exit? This study describes which supplier-specific metrics are most important to medical device manufacturers (MDMs) so they can utilize this supplier performance scorecard backed by real-world inputs. This research will focus on five categories to measure MDM supplier performance (Quality, …

Investigations Of External Resources And The Impact Of Imaging On Patient Flow In The Emergency Department, Marisa Shehan

All Theses

The problems associated with Emergency Department (ED) crowding are numerous, varied, and complex. Though overcrowded Emergency Departments are frequently attributed to overcrowded hospitals, crowding is also impacted by bottlenecks in patient flow. While discrete-event simulation (DES) is commonly used to model ED flow, external resources are typically excluded from these models due to their complexity and the limited amount of known information for these processes. Instead, external resources such as consults, labs, and imaging are modeled using estimation and/or educated guesswork. In this study, the impact of imaging on patient flow was assessed through data analysis of specific imaging factors, …

Examining The Impact Of Design Features Of Electronic Health Records Patient Portals On The Usability And Information Communication For Shared Decision Making, Rong Yin

All Dissertations

The use of the Electronic Health Records (EHR) patient portal has been shown to be effective in generating positive outcomes in patients’ healthcare, improving patient engagement and patient-provider communication. Government legislation also required proof of its meaningful use among patients by healthcare providers. Typical patient portals also include features such as health information and patient education materials. However, little research has examined the specific use of patient portals related to individuals with specific diseases such as inflammatory bowel diseases (IBDs). IBDs are life-long, not curable, chronic diseases that can impact the whole population. Individuals with IBDs may have higher needs …

Inpatient Discharge-By-Noon: Are Fewer Better Than All?, Nicholas Ballester, Pratik J. Parikh, Kara Combs, Jordan S. Peck

Journal of Maine Medical Center

Introduction: To address boarding in hospital emergency departments, discharge-by-noon could free up inpatient beds earlier in the day. However, discharging all patients by noon can heavily burden inpatient units and may not be feasible. In this study, we determine the number of discharges after which the benefits of an additional discharge-by-noon diminish.

Methods: We conducted a simulation analysis to quantify how occupancy rate, mean daily number of discharges, and peak discharge time impact upstream boarding time in an inpatient neurology unit at Maine Medical Center. Using a day-of-discharge simulation model with one year of retrospective data, we assessed configurations approximating …

Réduction Des Risques Lors Des Interventions En Espace Clos : Développement D’Une Base De Connaissances Sur La Prévention Intrinsèque Et La Protection Collective, Damien Burlet-Vienney, Yuvin Chinniah, Ali Bahloul, Andrés Felipe González Cortés, Capucine Ouellet, Abdallah Ben Mosbah

Rapports de recherche scientifique

Les espaces clos parmi les plus courants dans les milieux de travail au Québec sont les réservoirs, les cuves, les puits d’accès, les égouts, les tuyaux et les citernes. Les entrées en espace clos sont effectuées, entre autres, pour des raisons de maintenance (p. ex. : réparation, inspection, nettoyage, déblocage). Les risques pour la santé et la sécurité des travailleurs impliqués sont variés : atmosphérique, chimique, biologique, mécanique, physique ou liés à une chute ou au non-respect des principes ergonomiques. Dans le cadre de ce projet, une moyenne de 2,6 décès par an en espace clos au Québec sur la …

Assessing Patient Safety Culture In United States' Hospitals, Abdulmajeed Azyabi

Electronic Theses and Dissertations, 2020-2023

Patient safety is founded on continuous learning because there is an urgent need to report and learn from errors, accidents, near misses, and adverse events. The traditional approach to patient safety, based on forming mortality committees and investigating accidents, will no longer be effective. Frameworks, surveys, and assessment tools have been developed over the last decade to assist organizations in measuring and understanding their culture. This a retrospective cross-sectional study included 67,010 respondents from Agency for Health care Research and Quality (AHRQ) 2018 comparative database was analyzed using partial least squares structural equation modeling (PLS-SEM). This research explored whether the …

Developing Artificial Intelligence Tools To Investigate The Phenotypes And Correlates Of Chronic Kidney Disease Patients In West Virginia, Marzieh Amiri Shahbazi

Graduate Theses, Dissertations, and Problem Reports (ETD)

ABSTRACT

Developing Artificial Intelligence tools to investigate the phenotypes and correlates of Chronic Kidney Disease patients in West Virginia

Marzieh Amiri Shahbazi

Chronic kidney disease (CKD) is responsible for disrupting the lives of 37 million people just in the USA, which is about 1 in 7 adults. CKD results in a gradual loss of kidney function over time. Sometimes CKD doesn’t produce any significant symptoms until it reaches an advanced stage. On the other hand, acute kidney injury (AKI) accounts for a sudden decline in the kidney’s function. As a result, the kidneys fail to filter waste materials from the …

Framework Of Big Data Analytics In Real Time For Healthcare Enterprise Performance Measurements, Ahmed Mohamed

Electronic Theses and Dissertations, 2020-2023

Healthcare organizations (HCOs) currently have many information records about their patients. Yet, they cannot make proper, faster, and more thoughtful conclusions in many cases with their information. Much of the information is structured data such as medical records, historical data, and non-clinical information. This data is stored in a central repository called the Data Warehouse (DW). DW provides querying and reporting to different groups within the healthcare organization to support their future strategic initiatives. The generated reports create metrics to measure the organization's performance for post-action plans, not for real-time decisions. Additionally, healthcare organizations seek to benefit from the semi-structured …

Drug-Based Therapeutic Strategies For Covid-19-Infected Patients And Their Challenges, Khatereh Zarkesh, Elaheh Entezar-Almahdi, Parisa Ghasemiyeh, Mohsen Akbarian, Marzieh Bahmani, Shahrzad Roudaki, Rahil Fazlinejad, Soliman Mohammadi-Samani, Negar Firouzabadi, Majid Hosseini, Fatemeh Farjadian

Manufacturing & Industrial Engineering Faculty Publications

Emerging epidemic-prone diseases have introduced numerous health and economic challenges in recent years. Given current knowledge of COVID-19, herd immunity through vaccines alone is unlikely. In addition, vaccination of the global population is an ongoing challenge. Besides, the questions regarding the prevalence and the timing of immunization are still under investigation. Therefore, medical treatment remains essential in the management of COVID-19. Herein, recent advances from beginning observations of COVID-19 outbreak to an understanding of the essential factors contributing to the spread and transmission of COVID-19 and its treatment are reviewed. Furthermore, an in-depth discussion on the epidemiological aspects, clinical symptoms …

Multi-Stage Stochastic Optimization And Reinforcement Learning For Forestry Epidemic And Covid-19 Control Planning, Sabah Bushaj

Dissertations

This dissertation focuses on developing new modeling and solution approaches based on multi-stage stochastic programming and reinforcement learning for tackling biological invasions in forests and human populations. Emerald Ash Borer (EAB) is the nemesis of ash trees. This research introduces a multi-stage stochastic mixed-integer programming model to assist forest agencies in managing emerald ash borer insects throughout the U.S. and maximize the public benets of preserving healthy ash trees. This work is then extended to present the first risk-averse multi-stage stochastic mixed-integer program in the invasive species management literature to account for extreme events. Significant computational achievements are obtained using …